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How Distributed, Columnar Databases Improve Analytics


THURSDAY, JULY 19 - 11 am PT / 2 pm ET

The increased requirements of modern analytical workloads, querying billions of rows on demand, in real time and in unforeseen ways, is a challenge for relational databases because they're optimized for transactional workloads (e.g., point and range queries with indexes).

While transactional workload queries tend to be row-oriented (e.g., return every column in a single row), analytical workload queries tend to be column-oriented (e.g., return the aggregate of a single column in every row). By storing columns of data rather than rows of data, columnar databases optimize for analytical workloads without sacrificing the relational model and SQL.

In this webinar, we will use MariaDB AX, an open source columnar database, to explain how column-based storage improves query performance and storage efficiency, and how it can be combined with massively parallel processing and streaming ingestion to support scalable, high-performance analytics on near real-time data.

Register today for this discussion titled How Distributed, Columnar Databases Improve Analytics.

Audio is streamed over the Internet, so turn up your computer speakers!
Shane Johnson
Senior Director of Product Marketing
MariaDB Corporation
Stephen Faig
Research Director
Unisphere Research
and DBTA